
import unittest

from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch

			from transformers import (
			    LiltForQuestionAnswering,
			    LiltForSequenceClassification,
			    LiltForTokenClassification,
			    LiltModel,
			)
			from transformers.models.lilt.modeling_lilt import LILT_PRETRAINED_MODEL_ARCHIVE_LIST





class       _lowerCAmelCase   :




				def __init__(   self							,							_UpperCamelCase							,							_UpperCamelCase=13							,							_UpperCamelCase=7							,							_UpperCamelCase=True							,							_UpperCamelCase=True							,							_UpperCamelCase=True							,							_UpperCamelCase=True							,							_UpperCamelCase=99							,							_UpperCamelCase=24							,							_UpperCamelCase=2							,							_UpperCamelCase=6							,							_UpperCamelCase=37							,							_UpperCamelCase="gelu"							,							_UpperCamelCase=0.1							,							_UpperCamelCase=0.1							,							_UpperCamelCase=512							,							_UpperCamelCase=16							,							_UpperCamelCase=2							,							_UpperCamelCase=0.02							,							_UpperCamelCase=3							,							_UpperCamelCase=None							,							_UpperCamelCase=1_000							,							)							-> Optional[Any]:
							lowerCAmelCase_         =      parent
							lowerCAmelCase_         =      batch_size
							lowerCAmelCase_         =      seq_length
							lowerCAmelCase_         =      is_training
							lowerCAmelCase_         =      use_input_mask
							lowerCAmelCase_         =      use_token_type_ids
							lowerCAmelCase_         =      use_labels
							lowerCAmelCase_         =      vocab_size
							lowerCAmelCase_         =      hidden_size
							lowerCAmelCase_         =      num_hidden_layers
							lowerCAmelCase_         =      num_attention_heads
							lowerCAmelCase_         =      intermediate_size
							lowerCAmelCase_         =      hidden_act
							lowerCAmelCase_         =      hidden_dropout_prob
							lowerCAmelCase_         =      attention_probs_dropout_prob
							lowerCAmelCase_         =      max_position_embeddings
							lowerCAmelCase_         =      type_vocab_size
							lowerCAmelCase_         =      type_sequence_label_size
							lowerCAmelCase_         =      initializer_range
							lowerCAmelCase_         =      num_labels
							lowerCAmelCase_         =      scope
							lowerCAmelCase_         =      range_bbox




				def 	__a		(   self	)							-> Dict:
							lowerCAmelCase_         =      ids_tensor([self.batch_size, self.seq_length]							,							self.vocab_size	)

							lowerCAmelCase_         =      ids_tensor([self.batch_size, self.seq_length, 4]							,							self.range_bbox	)
							# Ensure that bbox is legal
							for i in range(bbox.shape[0]	):
										for j in range(bbox.shape[1]	):
													if bbox[i, j, 3] < bbox[i, j, 1]:
																lowerCAmelCase_         =      bbox[i, j, 3]
																lowerCAmelCase_         =      bbox[i, j, 1]
																lowerCAmelCase_         =      t
													if bbox[i, j, 2] < bbox[i, j, 0]:
																lowerCAmelCase_         =      bbox[i, j, 2]
																lowerCAmelCase_         =      bbox[i, j, 0]
																lowerCAmelCase_         =      t

							lowerCAmelCase_         =      None
							if self.use_input_mask:
										lowerCAmelCase_         =      ids_tensor([self.batch_size, self.seq_length]							,							vocab_size=2	)

							lowerCAmelCase_         =      None
							if self.use_token_type_ids:
										lowerCAmelCase_         =      ids_tensor([self.batch_size, self.seq_length]							,							self.type_vocab_size	)

							lowerCAmelCase_         =      None
							lowerCAmelCase_         =      None
							if self.use_labels:
										lowerCAmelCase_         =      ids_tensor([self.batch_size]							,							self.type_sequence_label_size	)
										lowerCAmelCase_         =      ids_tensor([self.batch_size, self.seq_length]							,							self.num_labels	)

							lowerCAmelCase_         =      self.get_config()

							return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels




				def 	__a		(   self	)							-> Optional[Any]:
							return LiltConfig(
							    vocab_size=self.vocab_size							,							hidden_size=self.hidden_size							,							num_hidden_layers=self.num_hidden_layers							,							num_attention_heads=self.num_attention_heads							,							intermediate_size=self.intermediate_size							,							hidden_act=self.hidden_act							,							hidden_dropout_prob=self.hidden_dropout_prob							,							attention_probs_dropout_prob=self.attention_probs_dropout_prob							,							max_position_embeddings=self.max_position_embeddings							,							type_vocab_size=self.type_vocab_size							,							initializer_range=self.initializer_range							,							)




				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							)							-> str:
							lowerCAmelCase_         =      LiltModel(config=_UpperCamelCase	)
							model.to(_UpperCamelCase	)
							model.eval()
							lowerCAmelCase_         =      model(_UpperCamelCase							,							bbox=_UpperCamelCase							,							attention_mask=_UpperCamelCase							,							token_type_ids=_UpperCamelCase	)
							lowerCAmelCase_         =      model(_UpperCamelCase							,							bbox=_UpperCamelCase							,							token_type_ids=_UpperCamelCase	)
							lowerCAmelCase_         =      model(_UpperCamelCase							,							bbox=_UpperCamelCase	)

							self.parent.assertEqual(result.last_hidden_state.shape							,							(self.batch_size, self.seq_length, self.hidden_size)	)
							self.parent.assertEqual(result.pooler_output.shape							,							(self.batch_size, self.hidden_size)	)




				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							)							-> int:
							lowerCAmelCase_         =      self.num_labels
							lowerCAmelCase_         =      LiltForTokenClassification(config=_UpperCamelCase	)
							model.to(_UpperCamelCase	)
							model.eval()
							lowerCAmelCase_         =      model(
							    _UpperCamelCase							,							bbox=_UpperCamelCase							,							attention_mask=_UpperCamelCase							,							token_type_ids=_UpperCamelCase							,							labels=_UpperCamelCase	)
							self.parent.assertEqual(result.logits.shape							,							(self.batch_size, self.seq_length, self.num_labels)	)




				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							)							-> int:
							lowerCAmelCase_         =      LiltForQuestionAnswering(config=_UpperCamelCase	)
							model.to(_UpperCamelCase	)
							model.eval()
							lowerCAmelCase_         =      model(
							    _UpperCamelCase							,							bbox=_UpperCamelCase							,							attention_mask=_UpperCamelCase							,							token_type_ids=_UpperCamelCase							,							start_positions=_UpperCamelCase							,							end_positions=_UpperCamelCase							,							)
							self.parent.assertEqual(result.start_logits.shape							,							(self.batch_size, self.seq_length)	)
							self.parent.assertEqual(result.end_logits.shape							,							(self.batch_size, self.seq_length)	)




				def 	__a		(   self	)							-> Union[str, Any]:
							lowerCAmelCase_         =      self.prepare_config_and_inputs()
							(
							    (
							    lowerCAmelCase_    
							)      ,    (
							    lowerCAmelCase_    
							)      ,    (
							    lowerCAmelCase_    
							)      ,    (
							    lowerCAmelCase_    
							)      ,    (
							    lowerCAmelCase_    
							)      ,    (
							    lowerCAmelCase_    
							)      ,    (
							    lowerCAmelCase_    
							)      ,        
							)         =      config_and_inputs
							lowerCAmelCase_         =      {
							    "input_ids": input_ids,
							    "bbox": bbox,
							    "token_type_ids": token_type_ids,
							    "attention_mask": input_mask,
							}
							return config, inputs_dict





@require_torch
class       _lowerCAmelCase   (      UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__   , unittest.TestCase			):
				_lowercase     						=(
				    (
				        LiltModel,
				        LiltForSequenceClassification,
				        LiltForTokenClassification,
				        LiltForQuestionAnswering,
				    )
				    if is_torch_available()
				    else ()
				)
				_lowercase     						=(
				    {
				        "feature-extraction": LiltModel,
				        "question-answering": LiltForQuestionAnswering,
				        "text-classification": LiltForSequenceClassification,
				        "token-classification": LiltForTokenClassification,
				        "zero-shot": LiltForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)
				_lowercase     						=False
				_lowercase     						=False




				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase	)							-> Optional[int]:
							return True




				def 	__a		(   self	)							-> Union[str, Any]:
							lowerCAmelCase_         =      LiltModelTester(self	)
							lowerCAmelCase_         =      ConfigTester(self							,							config_class=_UpperCamelCase							,							hidden_size=37	)




				def 	__a		(   self	)							-> List[Any]:
							self.config_tester.run_common_tests()




				def 	__a		(   self	)							-> List[Any]:
							lowerCAmelCase_         =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_UpperCamelCase	)




				def 	__a		(   self	)							-> str:
							lowerCAmelCase_         =      self.model_tester.prepare_config_and_inputs()
							for type in ["absolute", "relative_key", "relative_key_query"]:
										lowerCAmelCase_         =      type
										self.model_tester.create_and_check_model(*_UpperCamelCase	)




				def 	__a		(   self	)							-> List[Any]:
							lowerCAmelCase_         =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*_UpperCamelCase	)




				def 	__a		(   self	)							-> int:
							lowerCAmelCase_         =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*_UpperCamelCase	)




				@slow
				def 	__a		(   self	)							-> Union[str, Any]:



							for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										lowerCAmelCase_         =      LiltModel.from_pretrained(_UpperCamelCase	)
										self.assertIsNotNone(_UpperCamelCase	)





@require_torch
@slow
class       _lowerCAmelCase   (      unittest.TestCase			):




				def 	__a		(   self	)							-> int:
							lowerCAmelCase_         =      LiltModel.from_pretrained("SCUT-DLVCLab/lilt-roberta-en-base"	).to(_UpperCamelCase	)

							lowerCAmelCase_         =      torch.tensor([[1, 2]]							,							device=_UpperCamelCase	)
							lowerCAmelCase_         =      torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]]							,							device=_UpperCamelCase	)

							# forward pass
							with torch.no_grad():
										lowerCAmelCase_         =      model(input_ids=_UpperCamelCase							,							bbox=_UpperCamelCase	)

							lowerCAmelCase_         =      torch.Size([1, 2, 768]	)
							lowerCAmelCase_         =      torch.tensor(
							    [[-0.0653, 0.0950, -0.0061], [-0.0545, 0.0926, -0.0324]]							,							device=_UpperCamelCase							,							)

							self.assertTrue(outputs.last_hidden_state.shape							,							_UpperCamelCase	)
							self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :, :3]							,							_UpperCamelCase							,							atol=1e-3	)	)




'''simple docstring'''




import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


logging.set_verbosity_info()
__UpperCAmelCase					=logging.get_logger(__name__)






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)      ->       Optional[Any]:
    __lowerCamelCase            =    UniSpeechSatForSequenceClassification.from_pretrained(UpperCamelCase__							,    config=UpperCamelCase__							)
    __lowerCamelCase            =    downstream_dict['''projector.weight''']
    __lowerCamelCase            =    downstream_dict['''projector.bias''']
    __lowerCamelCase            =    downstream_dict['''model.post_net.linear.weight''']
    __lowerCamelCase            =    downstream_dict['''model.post_net.linear.bias''']
    return model






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)      ->       Union[str, Any]:
    __lowerCamelCase            =    UniSpeechSatForAudioFrameClassification.from_pretrained(UpperCamelCase__							,    config=UpperCamelCase__							)
    __lowerCamelCase            =    downstream_dict['''model.linear.weight''']
    __lowerCamelCase            =    downstream_dict['''model.linear.bias''']
    return model






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)      ->       List[Any]:
    __lowerCamelCase            =    UniSpeechSatForXVector.from_pretrained(UpperCamelCase__							,    config=UpperCamelCase__							)
    __lowerCamelCase            =    downstream_dict['''connector.weight''']
    __lowerCamelCase            =    downstream_dict['''connector.bias''']
    for i, kernel_size in enumerate(hf_config.tdnn_kernel							):
        __lowerCamelCase            =    downstream_dict[
            f"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
        ]
        __lowerCamelCase            =    downstream_dict[f"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

    __lowerCamelCase            =    downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
    __lowerCamelCase            =    downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
    __lowerCamelCase            =    downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
    __lowerCamelCase            =    downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
    __lowerCamelCase            =    downstream_dict['''objective.W''']
    return model






@torch.no_grad()
def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)      ->       Optional[Any]:
    __lowerCamelCase            =    torch.load(UpperCamelCase__							,    map_location='''cpu'''							)

    __lowerCamelCase            =    checkpoint['''Downstream''']

    __lowerCamelCase            =    UniSpeechSatConfig.from_pretrained(UpperCamelCase__							)
    __lowerCamelCase            =    WavaVecaFeatureExtractor.from_pretrained(
        UpperCamelCase__							,    return_attention_mask=UpperCamelCase__							,    do_normalize=UpperCamelCase__							)

    __lowerCamelCase            =    hf_config.architectures[0]
    if arch.endswith('''ForSequenceClassification'''							):
        __lowerCamelCase            =    convert_classification(UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)
    elif arch.endswith('''ForAudioFrameClassification'''							):
        __lowerCamelCase            =    convert_diarization(UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)
    elif arch.endswith('''ForXVector'''							):
        __lowerCamelCase            =    convert_xvector(UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)
    else:
        raise NotImplementedError(f"""S3PRL weights conversion is not supported for {arch}"""							)

    if hf_config.use_weighted_layer_sum:
        __lowerCamelCase            =    checkpoint['''Featurizer''']['''weights''']

    hf_feature_extractor.save_pretrained(UpperCamelCase__							)
    hf_model.save_pretrained(UpperCamelCase__							)


if __name__ == "__main__":
  __UpperCAmelCase					=argparse.ArgumentParser()
  parser.add_argument(
      "--base_model_name", default=None, type=str, help="Name of the huggingface pretrained base model."
  )
  parser.add_argument("--config_path", default=None, type=str, help="Path to the huggingface classifier config.")
  parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to the s3prl checkpoint.")
  parser.add_argument("--model_dump_path", default=None, type=str, help="Path to the final converted model.")
  __UpperCAmelCase					=parser.parse_args()
  convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
